Preparations

Murray’s tutorial notes pres 5.2 in docs folder Load the necessary libraries

# some useful packages for graphing
library(tidyverse) # for data wrangling
library(grid)
library(scales) # colour, size, axes etc. 
library(gridExtra)

Graphics infrastructure - essential elements of a graph

layers of data driven objects - at least 1 layer of data
coordinate system
scales - scale, legend, caption
faceting - multiple graphs - should look alike
themes - overall scheme of the graph - colour, size, font etc. etc. - aesthetics

Motivating data

head(BOD)

summary(BOD)

##       Time           demand     
##  Min.   :1.000   Min.   : 8.30  
##  1st Qu.:2.250   1st Qu.:11.62  
##  Median :3.500   Median :15.80  
##  Mean   :3.667   Mean   :14.83  
##  3rd Qu.:4.750   3rd Qu.:18.25  
##  Max.   :7.000   Max.   :19.80

making graphs the old way

# this is the long-hand version of making plots with ggplot but we never do it like this!!!! too long & messy
p <- ggplot() + # single layer - points
  layer(data=BOD, # data.frame
    mapping=aes(y=demand,x=Time),
    stat="identity", # use original data - identity means multiple the data point by 1 i.e. don't do anything to the raw data - plot as is
    geom="point", # plot data as points - geom says what geometric layer you want the data to display as
    position="identity", # where to place the points on the graph
    params = list(na.rm = TRUE),
    show.legend = FALSE # should there be a legend associated with this layer? e.g. axes scale, key etc.
  )+ # layer of lines - let's put another layer on our graph - a line!
  layer( data=BOD, #data.frame
    mapping=aes(y=demand,x=Time), # specific coordinates
    stat="identity", # use original data
    geom="line", # plot data as a line
    position="identity",
    params = list(na.rm = TRUE),
    show.legend = FALSE
  ) +
  coord_cartesian() + #cartesian coordinates
  scale_x_continuous() + #continuous x axis
  scale_y_continuous()  #continuous y axis

making graphs the new way

# short-hand version - much easier
ggplot(data=BOD, map=aes(y=demand,x=Time)) +  # aes means aesthetics
  geom_point()+
  geom_line()

# since the first argument in the ggplot function required is the data frame we can pipe our data to ggplot:
BOD %>% ggplot(map=aes(y=demand,x=Time))+
                 geom_point()+ # refer to layer by its geometric feature i.e. point
                 geom_line() # these additional layers inherit all the information from the ggplot function - but you can state separately

Layers

layers of data driven objects
- geometric objects to represent data
- statistical methods to summarize the data
- mapping of aesthetics
- position control

Primary geometric objects

`geom_bar`

Single continuous variable - HISTOGRAM

head(diamonds) # example data

ggplot(diamonds) + geom_bar(aes(x = carat)) # when you do geom_bar and you give it a continuous variable you get a histogram - needs to bin the data which you can specify if you want but generally don't

# Single categorical variable - BAR CHART

ggplot(diamonds) + geom_bar(aes(x = cut))

# Multiple categorical variables - STACKED BAR CHART IS DEFAULT

ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity)) # multiple categorical variables are stacked by default

# if you changed coordinate system to polar coordinates you'd get a pie chart!

ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity),
  position='dodge') # conditional bar char - bars are contingent on clarity - no longer stacked

`geom_boxplot`

good way to quickly visualise continuous data i.e. outliers, skewness etc.

univariate box plot

ggplot(diamonds) + geom_boxplot(aes(x = "carat", y = carat))

ggplot(diamonds) + geom_boxplot(aes(y = carat)) # this does the same thing - don't need to specify the x variable

# conditional box plot

ggplot(diamonds) + geom_boxplot(aes(x = cut, y = carat)) # multiple different categories

`geom_line`

ggplot(CO2) + geom_line(aes(x = conc, y = uptake)) # this looks like nonsense - let's plot things separately according to a grouping variable e.g. 'plant'

# Grouping

ggplot(CO2) + geom_line(aes(x = conc, y = uptake, group=Plant)) # this is much better - we have plotted each group separately

# Colour Scale

ggplot(CO2) + geom_line(aes(x = conc, y = uptake, colour=Plant)) # plot separately and colour each plot with a different colour & this also applies a legend! - standard now to represent things by two different scales to satisfy visual cues i.e. colour & shape, colour & line type

`geom_point`

Shape & Colour scale based on categorial variables in the data

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour=Treatment, shape=Treatment))

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour=Plant, shape=Type))

glimpse(CO2)

## Rows: 84
## Columns: 5
## $ Plant     <ord> Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn2, Qn2, Qn2, Qn2, Qn2,…
## $ Type      <fct> Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Que…
## $ Treatment <fct> nonchilled, nonchilled, nonchilled, nonchilled, nonchilled,…
## $ conc      <dbl> 95, 175, 250, 350, 500, 675, 1000, 95, 175, 250, 350, 500, …
## $ uptake    <dbl> 16.0, 30.4, 34.8, 37.2, 35.3, 39.2, 39.7, 13.6, 27.3, 37.1,…

`geom_smooth`

Useful as a diagnostic tool when contemplating what model to fit the data to BUT not appropriate for statistical analysis!!!!

ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake), method='lm') # runs a statistical model to fit a smoother to your data in the background (in this case the method is lm i.e. linear model) & plotting line of best fit & confidence intervals

## `geom_smooth()` using formula 'y ~ x'

# now add data to the smoothed plot
ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake), method='lm') +
  geom_point(aes(x=conc,y=uptake)) # we can see a linear model is not the best option for this dataset - so geom_smooth can be a good quick diagnostic tool when initially looking at your data to see what model might be best to approximate the data

## `geom_smooth()` using formula 'y ~ x'

# Smooth by grouping variable

# what if we smoothed by a grouping variable - this might help visualise the patterns??
ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake, group=Plant), method='lm') +
  geom_point(aes(x=conc,y=uptake))

## `geom_smooth()` using formula 'y ~ x'

# Smooth using a GAM instead of lm (i.e. different smoothing method)

ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake, group=Plant)) +
  geom_point(aes(x=conc,y=uptake)) # by not specifying the method, ggplot defaults to GAM (generalised linear model) as the model to fit

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_polygon`

Making maps - R has some good packages for this - sort of derived from GIS

`geom_tile`

`geom_raster`

EXAMPLES - GEOM_POINT

Store instructions for making a graph in ggplot

p <- ggplot(data=BOD)
p <- p + geom_point(aes(y=demand,x=Time))
p # this is doing print(p) - need to write print(p) if you're writing it in a function

Secondary geometric objects

`geom_errorbar`

`geom_pointrange`

Coordinate systems

Default in R: cartesion coordinates

ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
  coord_cartesian()

# Flip x & y

ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
 coord_flip()

# Polar coordinates - good for cyclical data

ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
  coord_polar()

# Orthographic coordinates - can change projections & reference system for maps

Scales

`scale_x_` and `scale_y_`

Naming axes

ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
  scale_x_continuous(name="CO2 conc") # what about superscripts? mathematical notation? greek letters?? we need a better way to name axes!

# we need to use an expression to do this - NB cannot have spaces in an expression!:
ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
    scale_x_continuous(name=expression(
      Ambient~CO[2]~concentration~(mg/l))) # ~ is a space, [] is subscript, ^ is superscript

# reference for how to write different characters - type demo(plotmath) into console and press enter to see all the different ways to get symbols

Expanding axes

ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
  scale_x_continuous(name="CO2 conc", expand=c(0,200)) # c(multiplier, addition) c(0,200) means 0 don't mulitiply scale but add 200 units - i.e. how to scale axes

# Axis on a log scale

ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
  scale_x_log10(name="CO2 conc",
    breaks=as.vector(c(1,2,5,10) %o% 10^(-1:2))) # breaks specifies where to put the tick marks & labels on the x axis

# 10^(-1:2) creates a vector of integers from 10^-1 to 10^2
# %o% operator means calculate the outer product so c(1,2,5,10) %o% 10^(-1:2)) gives you the outer product of c(1,2,5,10) & c(0.1,1,10,100) - we want to make the matrix result a vector so provide as.vector and that will give the vector required for where to put the tick marks

# if you change to log scale or square root scale you should identify the breaks / tick marks so it's more sensible

Scale categorical data

ggplot(CO2, aes(y=uptake,x=Treatment)) + geom_point()+
  scale_x_discrete(name="Treatment")

Other scales —————

size of points (thickness of lines)
shape of points
linetype of lines
color of lines or points
fill of shapes

`scale_size`

`scale_shape`

`scale_linetype`

`scale_fill` and `scale_color`

EXAMPLES - SCALES

(p <- p + scale_x_sqrt(name="Time")) # changes scale of axes (doesn't change the data) # NB you should plot raw data on transformed scales, not transformed data on linear scale - this is important! easier to read the graph & interpret raw data

(p <- p + scale_x_log10(name="Time"))

## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.

matrices of plots

facet_wrap

ggplot(CO2,aes(x=conc,y=uptake, colour=Type))+
  geom_point()+   
  facet_wrap(~Plant)+    # facet on the plant - we want a different plont for each plant type
  geom_smooth()    # smooth each graph

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# only time order matters is when each layer is drawn so smooth before points means the gray smooth is drawn before the points

Change scale for each graph in facet

ggplot(CO2)+geom_line(aes(x=conc,y=uptake, colour=Type))+ 
facet_wrap(~Plant, scales='free') # free scales means the scales appear on every single graph

ggplot(CO2)+geom_line(aes(x=conc,y=uptake, colour=Type))+ 
facet_wrap(~Plant, scales='free_y') # free scale only for y axis, free_x will do free axes only for x axis

# facet_grid

ggplot(CO2)+geom_point(aes(x=conc,y=uptake, colour=Type))+ 
facet_grid(Type~Treatment) # MUST have common x & y axes - can't differ axes like you can in facet_wrap

Themes

Default theme in R is theme_grey() type in theme_classic() into console to see all of the specified features - then you can see how to change certain bits

`theme_classic`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_linedraw() # just changes a bunch of stuff to make the graph look different which you can type out and code manually but these are just pre-packaged themes - can start with a theme and then make changes on top of that

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

theme_bw —————-

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_bw()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

theme_grey —————-

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_grey()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

theme_minimal —————-

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_minimal()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

theme_linedraw —————-

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_linedraw()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# changing specific parts of a theme

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_classic() + theme(axis.title.x=element_text(margin=margin(t=2,unit='pt')))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Practice ===========

EXAMPLES - GEOM_POINT WITH OPTIONAL MAPPING

Colour

ggplot(CO2) + 
  geom_point(aes(x=conc,y=uptake), colour="red")      # colour can be coded with words

ggplot(CO2) + 
  geom_point(aes(x=conc,y=uptake), color="#ff0000")   # colour can be coded with a hash code - refer to R colour chart for hash codes

# but there's no scale saying what the red dots mean!

Aesthetics & Scale

ggplot(CO2) + 
  geom_point(aes(x=conc,y=uptake, color="blue")) # anything INSIDE the aesthetic has a scale - if it's OUTSIDE the aesthetic it DOESN'T have a scale - if it's outside it's treated literally, if it's inside the brackets it's not treated literally, it says colour it according to what groups I have

# Graphing different statistics - input raw data, plot summary data

ggplot(CO2)+geom_point(aes(x=conc,y=uptake),
 stat="summary",fun.y=mean) # adds mean to the graph - passing raw data and plotting aggregated data - this isn't the best way to do things - you should aggregate your data first then pass directly to the graph

## Warning: Ignoring unknown parameters: fun.y

## No summary function supplied, defaulting to `mean_se()`

Exporting graphs ================ # how to export a pretty graph from R??????

# first store graph
g1 <- ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() + theme_linedraw()
ggsave(filename='/Users/sfulton/OneDrive - James Cook University/JCU/R/AIMSJCU ML R COURSE/ML R COURSE/testgraph.png', g1, width=5, height=5, units='cm',dpi=300) # default unit for size are in inches so can specify units if need be # can set resolution also

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# good to have a folder called output and then others inside that called tables, figures etc.

Introduction to grammar of graphics

Murray Logan

01 September, 2020